Open AccessCompressed sensing approach for pattern synthesis of maximally sparse non‐uniform linear array
Author(s) -
Zhao Xiaowen,
Yang Qingshan,
Zhang Yunhua
Publication year - 2014
Publication title -
iet microwaves, antennas and propagation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.555
H-Index - 69
eISSN - 1751-8733
pISSN - 1751-8725
DOI - 10.1049/iet-map.2013.0492
Subject(s) - compressed sensing , sparse array , algorithm , computer science , matching pursuit , sparse matrix , matching (statistics) , signal (programming language) , matrix (chemical analysis) , sparse approximation , mathematics , materials science , physics , statistics , quantum mechanics , composite material , gaussian , programming language
Compressed sensing (CS) has been successfully applied to the synthesis of maximally sparse non‐uniform linear array with the synthesised pattern matching the reference pattern very well by using as few elements as possible. According to the CS theory, a sparse or compressible high‐dimensional signal can be first projected onto a low‐dimensional space through a measurement matrix, and then recovered accurately by using a variety of practical algorithms based on the low‐dimensional information. The proposed approach can synthesise the sparse linear arrays fitting the desired patterns with a minimum number of elements. Numerical simulations validate the effectiveness and advantages of the proposed synthesis method. Moreover, compared with the existing sparse‐array synthesis methods, the author's method is more robust and accurate, while maintaining the advantage of easy implementation.